Counter-on-chip for bacterial cell quantification, growth, and live-dead estimations

Quantifying bacterial cell numbers is crucial for experimental assessment and reproducibility, but the current technologies have limitations. The commonly used colony forming units (CFU) method causes a time delay in determining the actual numbers. Manual microscope counts are often error-prone for submicron bacteria. Automated systems are costly, require specialized knowledge, and are erroneous when counting smaller bacteria. In this study, we took a different approach by constructing three sequential generations (G1, G2, and G3) of counter-on-chip that accurately and timely count small particles and/or bacterial cells. We employed 2-photon polymerization (2PP) fabrication technology; and optimized the printing and molding process to produce high-quality, reproducible, accurate, and efficient counters. Our straightforward and refined methodology has shown itself to be highly effective in fabricating structures, allowing for the rapid construction of polydimethylsiloxane (PDMS)-based microfluidic devices. The G1 comprises three counting chambers with a depth of 20 µm, which showed accurate counting of 1 µm and 5 µm microbeads. G2 and G3 have eight counting chambers with depths of 20 µm and 5 µm, respectively, and can quickly and precisely count Escherichia coli cells. These systems are reusable, accurate, and easy to use (compared to CFU/ml). The G3 device can give (1) accurate bacterial counts, (2) serve as a growth chamber for bacteria, and (3) allow for live/dead bacterial cell estimates using staining kits or growth assay activities (live imaging, cell tracking, and counting). We made these devices out of necessity; we know no device on the market that encompasses all these features.

Step 2: Initially, 0.5 mL 2% Tween-20 was used to flush the device from the inlet to the outlet.Tween-20 is a non-ionic surfactant that helps to prevent cell/particle adhesion to the PDMS surface 1,2 .Notably, the round-shaped microfluidic counter helps smooth particle/cell movement without sticking at the edge.
Step 3: Next, at least 0.2 mL of samples were loaded from the inlet using a 1 mL syringe (Fig. S1 i).The particles/cells stopped moving around ~20-25 mins (supplementary movies S1 and S2).Fig. S1 ii shows one chamber of the G1 with 5 µm microbeads.For counting 1 µm microbeads and E. coli cells (with G2 and G3) similar procedure (Steps 1-3) was applied.Microscopic imaging was taken with 100x magnification for G1, and 1000x for G2 and G3, as E. coli are relatively small, which needed higher magnification for precise imaging and subsequently counting.
Step 4: After the imaging was done, we initially conducted cell/bead quantification through manual counting, which was quite time-consuming and not user-friendly.Fiji ImageJ 3 is a very well-known image analysis software.Using this tool, we counted cell/bead and compared the results with manually counting data.We found an exact match.An example of the counted masked image is shown in Fig. S1 iii.For a small number of images this tool can be very handy.However, a large number of images requires additional expertise.In this case Fiji ImageJ macros record command could be an option 3 , but specific programming skill is required to get this done efficiently.We wanted to keep this counting simple, so we used our previously developed image-processing method that uses custom computer code and leverages machine-learning algorithms 4,[7][8][9] .The code will be found at https://github.com/hdeter/CountColonies.
Step. 5: To validate counting results, we performed random checks on images through manual counting, which showed a consistently low error rate, often falling below 1%.This underscores the accuracy and reliability of our counting methodology.

Fig. S2:
The G1 device showed counting accuracy with two different size beads.(i) For 5 µm beads, there was no statistically significant variation was observed among the three different chambers (p = 0.28, n = 8).The individual total count in each chamber (C1-C3) showed a significant count difference (C1 = 72.4%,C2 = 57.3%, and C3 = 63.6%)compared to the supplier counts.However, G1 (C1+C2+C3) showed less count discrepancy (6.6%) with no statistically significant variation (p = 0.12, n = 8) compared to Petroff-Hausser (PH) count.PH showed a count variation of 24.2% compared to supplier count (also see Fig. 2 c i). (ii) 1 µm beads were counted using two chambers (C1 and C2).As expected, the total count difference was found to be statistically significant (p = 0.003, n = 8).The C1 chamber count was varied by 29.2% and, C2 by 54.2% compared to the supplier counts.However, the combination of C1 and C2 chamber counts showed a 16% difference compared to the supplier count, with statistically significant count variation (p = 0.0004, n = 8).We observed these significant chamber-tochamber count differences because the chamber volume increased two-fold (see Fig. 2a).n: Number of replicates.Fig. S3: G3 maintained a high correlation and reduced count variation.(i) Strong correlation was observed between the average chamber count and total count in G3 (R 2 = 0.998, n = 3).n: Number of replicates.This suggests that the retention capacity of G3 is not random; it increases when the concentration is increased.However, it's essential to note that a minimum concentration is required to achieve accurate counts.(ii) The accuracy of counts in the G3 chamber is enhanced when a minimum number of microbeads are present.For instance, when the average chamber count is lower, the total count difference is smaller.Conversely, when the average chamber count is higher, the count difference is dramatically increased compared to the supplier count.This underscores the importance of maintaining an optimal concentration in the chamber.Similar to the other systems, our G3 showed accuracy in counting with an optimum range of concentrations.